Triple
T10958500
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Merdeka 118 |
E258905
|
entity |
| Predicate | regionalRankByHeight |
P87950
|
FINISHED |
| Object | tallest building in Southeast Asia |
—
|
LITERAL FINISHED |
How this triple was built (2 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: tallest building in Southeast Asia | Statement: [Merdeka 118, regionalRankByHeight, tallest building in Southeast Asia]
PD
Predicate disambiguation
gpt-5-mini-2025-08-07
Target predicate: regionalRankByHeight Context triple: [Merdeka 118, regionalRankByHeight, tallest building in Southeast Asia]
-
A.
regionRankByHeight
chosen
Indicates the relative ordering of regions based on their height or elevation.
-
B.
countryRankByHeightAtCompletion
Indicates the position of a country in an ordered list based on the height of something (typically a structure or project) at the time it was completed.
-
C.
rankByHeightWorld
Indicates an ordering of entities based on their relative height compared to all others in the world.
-
D.
countryHighestPointRank
Indicates the relative ranking of a country's highest natural elevation compared to the highest points of other countries.
-
E.
countryHighestPeaksRank
Indicates the relative ranking position of a country based on the heights of its highest peaks compared to those of other countries.
- F. None of above.
Provenance (3 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d6aa88500c819097d7032ca578e74f |
completed | April 8, 2026, 7:20 p.m. |
| NER | Named-entity recognition | batch_69d77126d9288190aa5daf2ba83d731d |
completed | April 9, 2026, 9:28 a.m. |
| PD | Predicate disambiguation | batch_69d72e874f48819096ffa878f90c7d5b |
completed | April 9, 2026, 4:43 a.m. |
Created at: April 8, 2026, 9:23 p.m.